Senior Data Science Analyst – AI Anti-Cheat & Game Integrity (Poker)

⚠️ Important Requirement — Poker Domain Expertise

Hands-on poker expertise is a strict and non-negotiable requirement for this role.

Senior Analyst – Data Science (AI Anti-Cheat, Poker)

Location: Remote / Global

Team: AI Anti-Cheat & Game Integrity

Employment Type: Full-time

Role Overview

We are hiring a Senior Analyst – Data Science (AI Anti-Cheat, Poker) to help build next-generation fraud detection and game integrity systems for large-scale online poker platforms.

This role sits at the intersection of data engineering, ML-ready feature engineering, reinforcement-learning-driven strategy analysis, and production anti-cheat monitoring.

You will collaborate closely with Data Scientists, RL researchers, ML engineers, and product teams to transform massive gameplay data into reliable, real-world fraud detection intelligence.

Hands-on poker expertise is a strict requirement for this role.

Key Responsibilities

Feature Engineering & Data Pipelines

• Design, build, and maintain scalable data pipelines for ingesting and transforming multi-modal gameplay, device, and network data.
• Partner with Data Scientists and RL researchers to develop high-value engineered features for detecting bots, collusion, and unfair play.
• Conduct exploratory data analysis (EDA) to uncover meaningful signals and improve detection model performance.

Model Monitoring & Performance

• Develop monitoring frameworks to track production model health, including precision, recall, and false-positive rates across regions and products.

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• Perform root-cause analysis for model drift, data pipeline failures, or abnormal spikes in flagged behavior.
• Collaborate with ML engineers to refine training datasets, labeling strategies, and evaluation metrics.

Cross-Functional Collaboration

• Act as a bridge between Data Science, Reinforcement Learning, Engineering, and Operations teams.
• Translate solver outputs, RL strategies, and gameplay signals into operational anti-cheat intelligence.
• Provide clear analytical insights and reporting to product leaders, executives, and partner operators.
• Support integration of AI anti-cheat capabilities into live poker platforms.

Operational Excellence

• Ensure data quality, governance, consistency, and compliance across massive datasets (billions of hands, device logs, clickstreams).
• Build dashboards and analytical reporting systems to monitor detection effectiveness and investigation outcomes.
• Support incident response and fraud investigations through deep gameplay pattern analysis and validation of model outputs.

Technical Requirements

Core Data & Engineering Skills

• Advanced SQL with experience optimizing queries on large-scale datasets.
• Strong Python for data processing, analysis, and feature engineering (Pandas, NumPy, PySpark).
• Experience building and maintaining ETL pipelines and distributed data systems (e.g., Spark, Kafka, Airflow).
• Solid understanding of ML evaluation metrics (precision, recall, F1, AUC) and monitoring models in production.

AI / ML Collaboration

• Familiarity with reinforcement learning concepts in strategic or competitive games.
• Ability to convert solver outputs or RL predictions into production-ready anti-cheat features.

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• Experience collaborating with Data Scientists and ML Engineers to iterate on model performance.

Data Infrastructure & Tooling

• Hands-on experience with cloud data warehouses (Snowflake, BigQuery, Redshift) and distributed storage (S3, GCS).
• Experience with data visualization and monitoring tools (Tableau, Grafana, Superset, or similar).
• Understanding of data governance, privacy, and security best practices.

Poker Domain Expertise — Required

• Demonstrated hands-on expertise in online poker, through professional play, long-term winning experience, or direct work in poker integrity / anti-fraud.
• Deep understanding of game theory optimal (GTO) concepts, including range construction, equilibrium strategies, EV/equity analysis, and solver interpretation.
• Ability to distinguish strategic variance vs. fraudulent, automated, or collusive behavior in real gameplay environments.

Preferred / Bonus Qualifications

• Experience in gaming anti-cheat, fraud detection, or trust & safety systems.
• Background in graph analysis, network relationship detection, or anomaly detection.
• Experience with real-time or near real-time analytics pipelines.
• Prior work within major online poker platforms or integrity teams.

Why Join Us

• Work on cutting-edge AI and reinforcement learning applied to real-world gaming integrity.
• Join a fully remote, global, high-caliber technical team.
• Competitive salary, performance bonus, and generous paid leave.
• Make a direct impact on fairness, security, and trust across large-scale poker ecosystems.